World journal of emergency medicine最新文献

Background: Research has revealed a relationship between atrial fibrillation (AF) and valvular heart disease; however, the causality remains largely unknown. This study explored whether a causal association between AF and non-rheumatic aortic valve disease (AVD) and mitral valve disease (MVD) could be found.

Methods: A two-sample Mendelian randomization (TSMR) method was applied to determine the causal effect of AF on AVD, mitral regurgitation, and MVD. The inverse variance weighted (IVW) method was used as the primary analytical approach, and several complementary analyses were conducted. Outliers were detected using the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and radial Mendelian randomization (MR) methods.

Results: Genetically predicted AF was found to be causally associated with the risk of MVD (odds ratio [OR]=1.001; 95% confidence interval [CI]: 1.000-1.001; P=1.33×10^-6) and mitral regurgitation (OR=1.001; 95% CI: 1.000-1.002; P=0.009). However, no significant causal associations between AF and AVD were detected (OR=1.000; 95% CI: 0.999-1.000; P=0.804). Causal effects were still detected, even after adjusting for potential risk factors or removing the identified outliers. Reverse MR analyses revealed no significant causal effect of valvular heart disease on AF.

Conclusion: Our findings demonstrate a positive causal association between AF, MVD, and mitral regurgitation, but not AVD. Further research and an aggressive AF management strategy should be explored as potential measures for preventing MVD.

Background: This study aimed to develop and validate a nomogram to estimate the probability of prolonged intensive care unit (ICU) stays.

Methods: Pediatric patients who underwent cardiac surgery were included, with data collected from the pediatric intensive care database. The datasets were randomly divided into a training set (75%) and a testing set (25%). A nomogram model was developed to predict prolonged ICU stays in the training set and then validated in the testing set.

Results: A total of 795 patients and 266 patients were assigned to the training and testing sets, respectively, with consistent variables. The nomogram developed from the training set included eight characteristics: age, systolic blood pressure, respiratory rate, bicarbonate, direct bilirubin, high-sensitivity C-reactive protein, international normalized ratio, and operation time. The area under the curve values of the nomogram in the training and testing sets were 0.812 and 0.736, respectively. The nomogram demonstrated excellent discrimination and calibration. Decision curve analysis showed that the use of the nomogram resulted in more favorable outcomes compared with the strategies of treating all or none of the patients.

Conclusion: This study presents a nomogram that may enable early identification of high-risk patients and facilitates tailored postoperative care and better outcomes after pediatric cardiac surgery.

Background: Fluid resuscitation in acute pancreatitis (AP) patients requires precise titration because both excess and insufficient volumes may worsen outcomes. This study aimed to develop a weight-normalized fluid balance index (FBI) and assess its association with in-hospital mortality in critically ill AP patients.

Methods: This retrospective cohort study utilized data from the MIMIC-IV 3.0 database and the emergency intensive care unit (EICU) of our hospital (validation cohort) and was based on inclusion and exclusion criteria. Using the R package cutoff, an FBI of 145 mL/kg was identified as the optimal risk stratification threshold. The primary outcome was in-hospital all-cause mortality. Machine learning was used to screen covariates for inclusion in multivariable Cox models. Cox regression and restricted cubic spline (RCS) models were used to evaluate the relationship between FBI and mortality. Propensity score matching (PSM) was applied to minimize baseline confounding. After PSM, Kaplan-Meier survival curves were generated, and the results were validated via data from our center.

Results: In this study, 547 AP patients from the MIMIC-IV database and 156 from the EICU of our hospital were included. In the MIMIC-IV cohort, the overall in-hospital mortality rate was 8.96%. Patients with FBI ≥145 mL/kg had significantly higher in-hospital mortality than did those with FBI <145 mL/kg (P<0.05). High-risk classification remained an independent predictor of death after full adjustment (hazard ratio [HR] 1.99, 95% confidence interval [95% CI]: 1.08-3.69). Post-PSM Kaplan-Meier analysis confirmed significantly higher in-hospital mortality in the high-risk group (P<0.05). This result was corroborated by our validation cohort. RCS analysis further demonstrated a non-linear increase in in-hospital mortality with increasing FBI values.

Conclusion: An FBI ≥145 mL/kg may be associated with increased in-hospital mortality in critically ill AP patients.

Background: While the α7 nicotinic acetylcholine receptor (α7 nAChR) is implicated in sepsis-associated encephalopathy (SAE), its pathophysiological contributions require further investigation.

Methods: SAE was induced in mice via cecal ligation and puncture (CLP), and microglia were treated with lipopolysaccharide (LPS). PHA-543613 (an α7 nAChR agonist) was used to activate α7 nAChR. To study the role of α7 nAChR in mitophagy and pyroptosis, caspase-1-deficient mice and PTEN-induced kinase 1 (PINK1) small interfering RNA (siRNA) were used. Cognitive function, cerebral oxygen extraction ratio (CERO₂), and brain tissue oxygen pressure (PbtO₂) were measured. Blood-brain barrier (BBB) integrity was evaluated via Evan's blue staining. Mitophagy, pyroptosis, and cytokine levels were analyzed via Western blotting and immunofluorescence.

Results: CLP or LPS treatment significantly down-regulated α7 nAChR protein expression in microglia. The administration of PHA-543613 to activate α7 nAChR not only restored its expression post-sepsis, but also notably decreased BBB permeability and mitigated cognitive deficits. Both α7 nAChR activation and caspase-1 knockout effectively suppressed microglial pyroptosis. The activation of α7 nAChR also promoted mitophagy in microglia. This led to an amelioration of brain tissue hypoxia, as shown by elevated PbtO₂ and reduced CERO₂ levels. The suppression of microglial pyroptosis by α7 nAChR was counteracted when mitophagy was inhibited through the siRNA-mediated silencing of PINK1.

Conclusion: The activation of α7 nAChR reduces pyroptosis by enhancing microglial mitophagy, thereby mitigating SAE.

Background: Large language models (LLMs) are being explored for disease prediction and diagnosis; however, their efficacy for early sepsis identification in emergency departments (EDs) remains unexplored. This study aims to evaluate MedGo, a novel medical LLM, as a decision-support tool for clinicians managing patients with suspected sepsis.

Methods: This retrospective study included anonymized medical records of 203 patients (mean age 79.9±10.2 years) with confirmed sepsis from a tertiary hospital ED between January 2023 and January 2024. MedGo performance across nine sepsis-related assessment tasks was compared with that of two junior (<3 years of experience) and two senior (>10 years of experience) ED physicians. Assessments were scored on a 5-point Likert scale for accuracy, comprehensiveness, readability, and case-analysis skills.

Results: MedGo demonstrated diagnostic performance comparable to that of senior physicians across most metrics, achieving a median Likert score of 4 in accuracy, comprehensiveness, and readability. MedGo significantly outperformed junior physicians (P<0.001 for accuracy and case-analysis skills). MedGo assistance significantly enhanced both junior (P<0.001) and senior (P<0.05) physicians' diagnostic accuracy. Notably, MedGo-assisted junior physicians achieved accuracy levels comparable to those of unassisted senior physicians. MedGo maintained consistent performance across varying sepsis severities.

Conclusion: MedGo shows significant diagnostic efficacy for sepsis and effectively supports clinicians in the ED, particularly enhancing junior physicians' performance. Our study highlights the potential of MedGo as a valuable decision-support tool for sepsis management, paving the way for specialized sepsis AI models.